st: Fixed effects

Dear All,
I got in touch this morning to ask help on the problem below, but was later on informed that some of my notations(Greek alphabets) where unreadable, so I re-submit the problem:
I would like to compare the cost-effectiveness of a regulatory regime used for inspections for two distinct groups of (small and large) firms. I intend to use a dummy (i.g287) coding for the (output) size of a firm and then compare two groups on the basis of differences in the intercepts and coefficients.
Cit = mi + (beta)xit + zit(gamma) + nt + (epsilon)it.
C= total cost of the inspection regime
m=constant
x= cost of the regime per unit of output
z= Y_TCOST10, agr_score10 and enforcement10 (i.e. a set of other variables related to regulatory performance of a firm)
n= dummy intercept(i.e. for i.g287). This is time-variant , allowing a firm to grow its operation from small to large scale in time.
epsilon= error term
I also added interactions with the dummy and run the model with intention of estimating directly both the coefficients for the group (dummy=0) and coefficients for the difference between the two groups. Output of the model (see below) suggest that there is no significant difference in the intercepts, but there is a significant difference in the coefficients for x and agr_score10.
I am not quite sure of whether model is suitable for the comparison I am trying to make and, if so how to interpret the results? In particular, how I should interpret the insignificant difference in the intercepts and highly significant coefficients on interactions terms for variables x and agr_score10, bearing in mind that the dummy represent the size of a firm in a group in this case?
Also, any particular issues that I need to pay an attention, if the model is does what I want?
I will be happy to provide any additional information or explanation that may be necessary.
. xtreg C i.gt287##c.x i.gt287##c.Y_TCOST10 i.gt287##c.agr_score10 i.gt287##c.enforcement10, fe
Fixed-effects (within) regression Number of obs = 474
Group variable: my_id Number of groups = 94
R-sq: within = 0.5648 Obs per group: min = 1
between = 0.9508 avg = 5.0
overall = 0.9316 max = 8
F(9,371) = 53.50
corr(u_i, Xb) = 0.3923 Prob > F = 0.0000
---------------------------------------------------------------------------------------
C | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
1.gt287 | -34127.98 24120.31 -1.41 0.158 -81557.65 13301.69
x | .0066293 .0078059 0.85 0.396 -.0087201 .0219787
|
gt287#c.x |
| .4754795 .1637735 2.90 0.004 .1534387 .7975203
|
Y_TCOST10 | .3695438 .502372 0.74 0.462 -.6183098 1.357398
|
gt287#c.Y_TCOST10 |
1 | -.2244589 .5022651 -0.45 0.655 -1.212102 .7631844
|
agr_score10 | -16.97173 18.80148 -0.90 0.367 -53.94256 19.99909
|
gt287#c.agr_score10 |
1 | 109.7228 23.18021 4.73 0.000 64.14173 155.3039
|
enforcement10 | -1.241843 31.77901 -0.04 0.969 -63.73141 61.24773
|
gt287#c.enforcement10 |
1 | -7.497396 33.53713 -0.22 0.823 -73.4441 58.44931
|
_m | 37718.32 19743.62 1.91 0.057 -1105.108 76541.75
----------------------+----------------------------------------------------------------
sigma_u | 33442.826
sigma_e | 30638.016
rho | .54368618 (fraction of variance due to u_i)
---------------------------------------------------------------------------------------
F test that all u_i=0: F(93, 371) = 4.62 Prob > F = 0.0000
Thanks,
Mohamud
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